The task of analyzing very complex matrices of a plurality of variables and producing associative or predictive data in response thereto, essentially a task of pattern recognition and association, has proven extremely difficult for conventional electronic processing and storage techniques. Nevertheless, living organisms seem to perform this function easily. The present state of the art, as known and appreciated, appears to be characterized by numerous, common deficiencies. There is a lack of an efficient method for recognizing simple patterns over a wide range of scale, position, and form. There is also a lack of an efficient process for referencing a large body of stored data in order to determine how a present pattern of variables relates to previously received and stored patterns. There also appears to be a dependance on highly structured and organized integrated circuit networks that are susceptible to random manufacturing defects.
The commonly proposed approaches for selection or recognition of a pattern of values can apparently be characterized as pattern classification networks of the fixed type or of the adaptive type. The fixed, or "electronic template" pattern recognition network is generally able only to classify each area of a large field of data according to the resemblance of the data to a fixed "template" of circuit parameters using a fixed criterion or threshold level that is determined by the circuit design. An example of a template network is depicted in the Clapper U.S. Pat. No. 3,539,994. The adaptive network, on the other hand, is a self organizing or "learning" network which produces distinct outputs or resultants in response to different patterns and which, after sufficient repetition, is able to recognize a particular pattern. Adaptive networks of the prior art, such as depicted in U.S. Pat. Nos. 3,287,649 to Rosenblatt; 3,165,644 to Clapper; 3,103,648 to Hartmanis; and 3,273,125 to Jakowatz, show a degree of flexibility in the classification process. However, these networks generally require long "training periods" and are still primarily classification devices, capable of performing no other function.